lecture 2: linear and mixed models - university of washington

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Lecture 2:Linear and Mixed Models

Bruce Walsh lecture notesIntroduction to Mixed Models

SISG (Module 12), Seattle17 – 19 July 2019

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Quick Review of the Major Points

The general linear model can be written as

y = Xb + e• y = vector of observed dependent values

• X = Design matrix: observations of the variables in the assumed linear model

• b = vector of unknown parameters to estimate

• e = vector of residuals (deviation from model fit),e = y-X b

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y = Xb + eSolution to b depends on the covariance structure(= covariance matrix) of the vector e of residuals

• OLS: e ~ MVN(0, s2 I)• Residuals are homoscedastic and uncorrelated,

so that we can write the cov matrix of e as Cov(e) = s2I• the OLS estimate, OLS(b) = (XTX)-1 XTy

Ordinary least squares (OLS)

• GLS: e ~ MVN(0, V)• Residuals are heteroscedastic and/or dependent,• GLS(b) = (XT V-1 X)-1 XTV-1y

Generalized least squares (GLS)

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BLUE

• Both the OLS and GLS solutions are also called the Best Linear Unbiased Estimator (or BLUE for short)

• Whether the OLS or GLS form is used depends on the assumed covariance structure for the residuals– Special case of Var(e) = se

2 I -- OLS– All others, i.e., Var(e) = R -- GLS

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Linear ModelsOne tries to explain a dependent variable y as a linearfunction of a number of independent (or predictor)variables.

A multiple regression is a typical linear model,

Here e is the residual, or deviation between the truevalue observed and the value predicted by the linearmodel.

The (partial) regression coefficients are interpretedas follows: a unit change in xi while holding allother variables constant results in a change of bi in y

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Linear Models

As with a univariate regression (y = a + bx + e), the modelparameters are typically chosen by least squares,wherein they are chosen to minimize the sum ofsquared residuals, S ei

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This unweighted sum of squared residuals assumes an OLS error structure, so all residuals are equallyweighted (homoscedastic) and uncorrelated

If the residuals differ in variances and/or some arecorrelated (GLS conditions), then we need to minimize the weighted sum eTV-1e, which removes correlations andgives all residuals equal variance.

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Linear Models in Matrix FormSuppose we have 3 variables in a multiple regression,with four (y,x) vectors of observations.

The design matrix X. Details of both the experimental design and the observed values of the predictor variables all reside solely in X

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Rank of the design matrix• With n observations and p unknowns, X is an n x p

matrix, so that XTX is p x p• Thus, at most X can provide unique estimates for up

to p < n parameters• The rank of X is the number of independent rows of

X. If X is of full rank, then rank = p• A parameter is said to be estimable if we can provide

a unique estimate of it. If the rank of X is k < p, then exactly k parameters are estimable (some as linear combinations, e.g. b1-3b3 = 4)

• if det(XTX) = 0, then X is not of full rank• Number of nonzero eigenvalues of XTX gives the

rank of X.

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Experimental design and X• The structure of X determines not only which

parameters are estimable, but also the expected sample variances, as Var(b) = k (XTX)-1

• Experimental design determines the structure of X before an experiment (of course, missing data almost always means the final X is different form the proposed X)

• Different criteria used for an optimal design. Let V = (XTX)-1 . The idea is to chose a design for X given the constraints of the experiment that: – A-optimality: minimizes tr(V)– D-optimality: minimizes det(V)– E-optimality: minimizes leading eigenvalue of V

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Ordinary Least Squares (OLS)When the covariance structure of the residuals has acertain form, we solve for the vector b using OLS

If the residuals are homoscedastic and uncorrelated,s2(ei) = se

2, s(ei,ej) = 0. Hence, each residual is equallyweighted,

Sum of squaredresiduals canbe written as

If residuals follow a MVN distribution, OLS = ML solution

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Ordinary Least Squares (OLS)

Taking (matrix) derivatives shows this is minimized by

This is the OLS estimate of the vector b

The variance-covariance estimate for the sample estimatesis

The ij-th element gives the covariance between theestimates of bi and bj.

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Sample Variances/CovariancesThe residual variance can be estimated as

The estimated residual variance can be substituted into

To give an approximation for the sampling variance and covariances of our estimates.

Confidence intervals follow since the vector of estimates ~ MVN(b, Vb)

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Example: Regression Through the Originyi = bxi + ei

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Polynomial Regressions

GLM can easily handle any function of the observedpredictor variables, provided the parameters to estimateare still linear, e.g. Y = a + b1f(x) + b2g(x) + … + e

Quadratic regression:

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Interaction EffectsInteraction terms (e.g. sex x age) are handled similarly

With x1 held constant, a unit change in x2 changes yby b2 + b3x1 (i.e., the slope in x2 depends on the currentvalue of x1 )

Likewise, a unit change in x1 changes y by b1 + b3x2

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The GLM lets you build your own model!

• Suppose you want a quadratic regression forced through the origin where the slope of the quadratic term can vary over the sexes (pollen vs. seed parents)

• Yi = b1xi + b2xi2 + b3sixi

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• si is an indicator (0/1) variable for the sex (0 = male, 1 = female).– Male slope = b2,– Female slope = b2 + b3

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Generalized Least Squares (GLS)Suppose the residuals no longer have the samevariance (i.e., display heteroscedasticity). Clearlywe do not wish to minimize the unweighted sumof squared residuals, because those residuals withsmaller variance should receive more weight.

Likewise in the event the residuals are correlated,we also wish to take this into account (i.e., performa suitable transformation to remove the correlations)before minimizing the sum of squares.

Either of the above settings leads to a GLS solutionin place of an OLS solution.

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In the GLS setting, the covariance matrix for thevector e of residuals is written as R where Rij = s(ei,ej)

The linear model becomes y = Xb + e, cov(e) = R

The GLS solution for b is

The variance-covariance of the estimated model parameters is given by

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Model diagnostics• It’s all about the residuals• Plot the residuals

– Quick and easy screen for outliers– Plot y or yhat on e

• Test for normality among estimated residuals– Q-Q plot– Wilk-Shapiro test– If non-normal, try transformations, such as log

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OLS, GLS summary

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Fixed vs. Random EffectsIn linear models are are trying to accomplish two goals:estimation the values of model parameters and estimateany appropriate variances.

For example, in the simplest regression model, y = a + bx + e, we estimate the values for a and b and also the variance of e. We, of course, can alsoestimate the ei = yi - (a + bxi )

Note that a/b are fixed constants are we trying toestimate (fixed factors or fixed effects), while theei values are drawn from some probability distribution(typically Normal with mean 0, variance s2

e). The ei are random effects.

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“Mixed” models (MM) contain both fixed and random factors

This distinction between fixed and random effects isextremely important in terms of how we analyzed a model.If a parameter is a fixed constant we wish to estimate,it is a fixed effect. If a parameter is drawn fromsome probability distribution and we are trying to makeinferences on either the distribution and/or specific realizations from this distribution, it is a random effect.

We generally speak of estimating fixed factors (BLUE) andpredicting random effects (BLUP -- best linear unbiasedPredictor)

y = Xb + Zu + e, u ~MVN(0,R), e ~ MVN(0,s2eI)

Key: need to specify covariance structures for MM

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Random effects models

• It is often useful to treat certain effects as random, as opposed to fixed– Suppose we have k effects. If we treat these as

fixed, we lose k degrees of freedom– If we assume each of the k realizations are drawn

from a normal with mean zero and unknown variance, only one degree of freedom lost --- that for estimating the variance

• We can then predict the values of the k realizations

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Environmental effects• Consider yield data measured over several years in a

series of plots.• Standard to treat year-to-year variation at a specific

site as being random effects• Often the plot effects (mean value over years) are

also treated as random.• For example, consider plants group in growing

region i, location j within that region, and year (season) k for that location-region effect– E = Ri + Lij + eijk

– Typically R can be a fixed effect, while L and e are random effects, Lik ~ N(0,s2

L) and eikj ~ N(0,s2e)

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Random models• With a random model, one is assuming that

all “levels” of a factor are not observed. Rather, some subset of values are drawn from some underlying distribution– For example, year to year variation in rainfall at a

location. Each year is a random sample from the long-term distribution of rainfall values

– Typically, assume a functional form for this underlying distribution (e.g., normal with mean 0) and then use observations to estimate the distribution parameters (here, the variance)

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Random models (cont)• Key feature:

– Only one degree of freedom used (estimate of the variance)

– Using the fixed effects and the estimated underlying distribution parameters, one then predicts the actual realizations of the individual values (i.e., the year effects)

– Assumption: the covariance structure among the individual realizations of the realized effects. If only a variance is assume, this implies they are independent. If they are assumed to be correlated, this structure must be estimated.

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Random models• Let’s go back to treating yearly effects as random• If assume these are uncorrelated, only use one

degree of freedom, but makes assumptions about covariance structure– Standard: Uncorrelated– Option: some sort of autocorrelation process, say with a

yearly decay of r (must also be estimated)• Conversely, could all be treated as fixed, but would

use k degrees of freedom for k years, but no assumptions on their relationships (covariance structure)

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Identifiability

• Recall that a fixed effect is said to be estimable if we can obtain a unique estimate for it (either because X is of full rank or when using a generalized inverse it returns a unique estimate)– Lack of estimable arises because the experiment

design confounds effects• The analogous term for random models is

identifiability– The variance components have unique estimates

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y = Xb + Zu + e

The general mixed model

Vector of observations (phenotypes)

Vector of fixed effects (to be estimated), e.g., year, sex and age effects

Vector of random effects, such as

individual Breeding values (to be estimated)

Vector of residual errors(random effects)

Incidence matrix for fixed effects

Incidence matrix for random effects

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y = Xb + Zu + e

The general mixed model

Vector of observations (phenotypes)

Vector of random effects

Incidence matrix for fixed effects

Vector of fixed effects

Incidence matrix for random effects

Vector of residual errors

Observe y, X, Z.

Estimate fixed effects b

Estimate random effects u, e

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Means: E(u) = E(e) = 0, E(y) = Xb

Let R be the covariance matrix for the residuals. We typically assume R = s2

e*I

Let G be the covariance matrix for the vectoru of random effects

The covariance matrix for y becomes V = ZGZT + R

Means & Variances for y = Xb + Zu + e

Variances:

Hence, y ~ MVN (Xb, V)

Mean Xb due to fixed effectsVariance V due to random effects

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Estimating fixed Effects & Predicting Random Effects

For a mixed model, we observe y, X, and Z

b, u, R, and G are generally unknown

Two complementary estimation issues

(i) Estimation of b and u

Estimation of fixed effects

Prediction of random effects

BLUE = Best Linear Unbiased Estimator

BLUP = Best Linear Unbiased PredictorRecall V = ZGZT + R

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Different statistical models

• GLM = general linear model– OLS ordinary least squares: e ~ MVN(0,cI)– GLS generalized least squares: e ~ MVN(0,R)

• Mixed models– Both fixed and random effects (beyond the residual)

• Mixture models– A weighted mixture of distributions

• Generalized linear models– Nonlinear functions, non-normality

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Mixture models• Under a mixture model, an observation potentially

comes from one of several different distributions, so that the density function is p1f1 + p2f2 + p3f3– The mixture proportions pi sum to one – The fi represent different distribution, e.g., normal with mean µi

and variance s2

• Mixture models come up in QTL mapping -- an individual could have QTL genotype QQ, Qq, or qq– See Lynch & Walsh Chapter 13

• They also come up in codon models of evolution, were a site may be neutral, deleterious, or advantageous, each with a different distribution of selection coefficients– See Walsh & Lynch (volume 2A website), Chapters 10,11

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Generalized linear models

Typically assume non-normal distribution forresiduals, e.g., Poisson, binomial, gamma, etc